Journal of Transportation Research

Journal of Transportation Research

The Optimal Model of Dynamic Toll Pricing in Urban Freeways

Document Type : Original Article

Authors
Shahriar Afandizadeh 1
Mehdi Ganj Khanloo 2
Hamid Mirzahossein 3
1 Professor, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran.
2 M.Sc., Grad., School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran.
3 Associate Professor, Department of Civil - Transportation Planning, Imam Khomeini International University, Qazvin, Iran.
10.22034/tri.2023.375889.3107
Abstract
Collecting tolls is one of the solutions favored by transportation planners in dealing with traffic congestion. In this paper, urban tolls for internal freeways are presented in the form of a two-level optimization model. At the top, the decision level of the freeway toll is placed, and at the bottom, the issue of travel characteristics in the road network is solved to increase accuracy and efficiency. Allocation of travel from two aspects of network performance and social justice has been done dynamically, considering different population groups in terms of value. Also, this article considers a novel dynamic allocation model that uses the accuracy of micro-view and the speed of macro-view. To solve the problem in this multi-population metaheuristic algorithm has been developed. The combined multi-population method strengthens the local and global search operations to divide the population into different sub-populations. The teaching learning-based optimization (TLBO) is used in the presented method. The results showed that the speed and accuracy of the developed model are superior to the three competing algorithms. This algorithm reduced the travel time of the entire network from 8205 hours to 6683 hours, equivalent to 18% of the total travel time, and also, the speed of the presented algorithm has increased six times compared to the genetic algorithm.
Keywords
Toll Pricing
Teaching learning-based
Optimization (TLBO)
Dynamic Traffic Assignment
Metaheuristic Algorithm
Subjects
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